We base our pedestrian detector on Faster R-CNN [1]}, a popular state-of-the-art CNN-based object detection system belonging to the two-stage paradigm, already described in sec:back:cnn-based-detectors:two-stages-detectors, and that we also exploited in ch:virtual-to-real and ch:counting-with-uncertainty. We preferred a two-stage detector to single-shot detectors, as we could easily extract features from the region proposal stage and make them directly available for subsequent processing. We used the extracted features when tracking pedestrians, and they could be used in other future modules, such as cross-camera pedestrian re-identification. Moreover, Faster R-CNN is widely adopted and usually guarantees a state-of-the-art detection performance. However, the modularity of our system does not limit us to Faster R-CNN, and another object detector can be easily adopted in the future, just replacing the related module.
